/* --------------------------------------------------------------------------
CppAD: C++ Algorithmic Differentiation: Copyright (C) 2003-17 Bradley M. Bell

CppAD is distributed under multiple licenses. This distribution is under
the terms of the
                    Eclipse Public License Version 1.0.

A copy of this license is included in the COPYING file of this distribution.
Please visit http://www.coin-or.org/CppAD/ for information on other licenses.
-------------------------------------------------------------------------- */

/*
$begin abs_eval.cpp$$
$spell
	eval
$$

$section abs_eval: Example and Test$$

$head Purpose$$
The function
$latex f : \B{R}^3 \rightarrow \B{R}$$ defined by
$latex \[
	f( x_0, x_1, x_2  ) = | x_0 + x_1 | + | x_1 + x_2 |
\] $$
is affine, except for its absolute value terms.
For this case, the abs_normal approximation should be equal
to the function itself.

$head Source$$
$srcfile%example/abs_normal/abs_eval.cpp%
	0%// BEGIN C++%// END C++%
1%$$

$end
-------------------------------------------------------------------------------
*/
// BEGIN C++
# include <cppad/cppad.hpp>
# include "abs_eval.hpp"

namespace {
	CPPAD_TESTVECTOR(double) join(
		const CPPAD_TESTVECTOR(double)& x ,
		const CPPAD_TESTVECTOR(double)& u )
	{	size_t n = x.size();
		size_t s = u.size();
		CPPAD_TESTVECTOR(double) xu(n + s);
		for(size_t j = 0; j < n; j++)
			xu[j] = x[j];
		for(size_t j = 0; j < s; j++)
			xu[n + j] = u[j];
		return xu;
	}
}
bool abs_eval(void)
{	bool ok = true;
	//
	using CppAD::AD;
	using CppAD::ADFun;
	//
	typedef CPPAD_TESTVECTOR(double)       d_vector;
	typedef CPPAD_TESTVECTOR( AD<double> ) ad_vector;
	//
	double eps99 = 99.0 * std::numeric_limits<double>::epsilon();
	//
	size_t n = 3; // size of x
	size_t m = 1; // size of y
	size_t s = 2; // number of absolute value terms
	//
	// record the function f(x)
	ad_vector ad_x(n), ad_y(m);
	for(size_t j = 0; j < n; j++)
		ad_x[j] = double(j + 1);
	Independent( ad_x );
	// for this example, we ensure first absolute value is | x_0 + x_1 |
	AD<double> ad_0 = abs( ad_x[0] + ad_x[1] );
	// and second absolute value is | x_1 + x_2 |
	AD<double> ad_1 = abs( ad_x[1] + ad_x[2] );
	ad_y[0]         = ad_0 + ad_1;
	ADFun<double> f(ad_x, ad_y);

	// create its abs_normal representation in g, a
	ADFun<double> g, a;
	f.abs_normal_fun(g, a);

	// check dimension of domain and range space for g
	ok &= g.Domain() == n + s;
	ok &= g.Range()  == m + s;

	// check dimension of domain and range space for a
	ok &= a.Domain() == n;
	ok &= a.Range()  == s;

	// --------------------------------------------------------------------
	// Choose a point x_hat
	d_vector x_hat(n);
	for(size_t j = 0; j < n; j++)
		x_hat[j] = double(j - 1);

	// value of a_hat = a(x_hat)
	d_vector a_hat = a.Forward(0, x_hat);

	// (x_hat, a_hat)
	d_vector xu_hat = join(x_hat, a_hat);

	// value of g[ x_hat, a_hat ]
	d_vector g_hat = g.Forward(0, xu_hat);

	// Jacobian of g[ x_hat, a_hat ]
	d_vector g_jac = g.Jacobian(xu_hat);

	// value of delta_x
	d_vector delta_x(n);
	delta_x[0] =  1.0;
	delta_x[1] = -2.0;
	delta_x[2] = +2.0;

	// value of x
	d_vector x(n);
	for(size_t j = 0; j < n; j++)
		x[j] = x_hat[j] + delta_x[j];

	// value of f(x)
	d_vector y = f.Forward(0, x);

	// value of g_tilde
	d_vector g_tilde = CppAD::abs_eval(n, m, s, g_hat, g_jac, delta_x);

	// should be equal because f is affine, except for abs terms
	ok &= CppAD::NearEqual(y[0], g_tilde[0], eps99, eps99);

	return ok;
}
// END C++
